Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes

Understand

Few-shot segmentation~(FSS) performance has been extensively promoted by introducing episodic training and class-wise prototypes.

• However, the FSS problem remains challenging due to three limitations: (1) Models are distracted by task-unrelated information; (2) The representation ability of a single prototype is limited; (3) Class-related prototypes ignore the prior knowledge of base classes.
• We propose the Prior-Enhanced network with Meta-Prototypes to tackle these limitations.
• The prior-enhanced network leverages the support and query (pseudo-) labels in feature extraction, which guides the model to focus on the task-related features of the foreground objects, and suppress much noise due to the lack of supervised knowledge.